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Paul SavariappanView profile

Teaching Professor

• Queueing Theory
• Reliability Analysis
• Statistical Inference
• +3 more

Paul Savariappan is an Associate Teaching Professor in the Department of Statistics at North Carolina State University (NC State), where he has served since January 2018. Previously, he held roles including Visiting Associate Professor at the University of Wisconsin-Madison (2015-2016), Associate Professor at Luther College (2012-2017), and faculty positions at Loyola College, India (1986-2003). He holds a Ph.D. in Statistics from the University of Madras (2000), an M.S. in Biomathematics from Marquette University (2006), and multiple advanced degrees from Indian institutions. Education: Ph.D. in Statistics, University of Madras, 2000 M.S. in Biomathematics, Marquette University, 2006 M.Phil. in Statistics, University of Madras, 1991 M.Sc. in Statistics, Loyola College, 1984 B.Sc. in Statistics, St. Joseph’s College, 1982 Research Interests: Dr. Savariappan specializes in Queueing Theory , focusing on tandem queues, blocking mechanisms, and service rate analysis. His work in Reliability Analysis involves bivariate/trivariate exponential models and system dependability. He applies statistical methods to engineering and biological systems, with a strong emphasis on Bayesian inference and stochastic processes. Articles & Trends: His publications emphasize theoretical and applied aspects of queueing systems and reliability engineering. Recent work explores statistical inference for complex queueing networks and multivariate exponential distributions in system reliability. Earlier contributions address bulk arrival queues and maintenance optimization strategies. Awards: Three-time recipient of NC State’s “Thank a Teacher” award “Thank an Advisor” recognition from the Advising Professional Development Committee Advising & Grants: He advises over 60 students annually and co-advises master’s theses (e.g., Fernando David Soler Diaz’s work in Animal Science). He contributed to a USDA NIFA grant (2025) and developed NC State’s ST525 course for the Agriculture and Data Science certificate program. His service includes department diversity initiatives and peer teaching evaluations. Labs & Collaborations: Active in interdisciplinary projects, including data science collaborations with Duke University’s Data Fest. His work bridges statistics with engineering, biology, and agriculture through applied statistical methodologies.

Nir LipovetzkyView profile

Associate Professor

• AI Planning
• Search
• Learning
• +6 more

Nir Lipovetzky is an Associate Professor in Artificial Intelligence at the School of Computing and Information Systems, University of Melbourne. He is affiliated with the Agent Lab and Digital Agriculture, Food and Wine Lab. His research focuses on AI planning, search, and applications in autonomous systems, particularly in agriculture. He holds a PhD from Universitat Pompeu Fabra (2012) and a BSc in Computer Science (2004). Education: PhD in Artificial Intelligence, Universitat Pompeu Fabra (2012) MEng in Artificial Intelligence, Universitat Pompeu Fabra (2007) BSc in Computer Science, Universitat Pompeu Fabra (2004) Graduate Certificate in University Teaching, University of Melbourne (2020) Research interests include AI planning, constraint programming, operations research, and intention recognition. He has developed novel width-based planning algorithms and contributed to planning competitions. Recent work includes adaptive goal recognition and reinforcement learning for decision-making under uncertainty. Publications span topics like count-based novelty exploration, process mining for goal recognition, and human-like goal inference models. Awards include the Best Student Paper Award at AAMAS 2024 and recognition in planning competitions. Advises 10+ PhD and Master’s students on topics like exploration methods, agricultural robotics, and multi-agent systems. Supervises Farm.bot, an open-source platform for AI-driven agriculture. Serves as Conference Chair for ICAPS 2025 and has held roles in AAAI, IJCAI, and ICAPS committees.

• Statistical Ecology
• Wildlife Population Dynamics
• Bayesian Statistics
• +4 more

Eleni Matechou is an Honorary Academic in Statistics at the University of Kent, affiliated with the School of Mathematics, Statistics and Actuarial Science. Her academic background includes a BSc in Statistics from Athens University of Economics and Business, an MSc in Statistics with Applications in Medicine from the University of Southampton, and a PhD jointly conducted at the University of Kent and the Max Planck Institute for Demographic Research. She has held positions such as Research and Teaching Fellow at Victoria University of Wellington, Departmental Lecturer at the University of Oxford, and is currently a member of the Statistical Ecology @ Kent (SE@K) group and the National Centre for Statistical Ecology (NCSE). Her research focuses on statistical ecology, particularly developing models for wildlife populations, migration patterns, and environmental DNA (eDNA) monitoring. Key areas include Bayesian and frequentist approaches to capture-recapture models, mixture models, and integrating citizen science data for ecological inference. She is actively involved in methodological advancements for biodiversity assessment and conservation, emphasizing the use of innovative technologies like robotics and eDNA. Professionally, Eleni chairs the Environmental Statistics Section of the Royal Statistical Society and serves as Book Reviews Editor for the Journal of Agricultural, Biological and Environmental Statistics . Her work bridges statistical rigor with ecological applications, addressing challenges in monitoring biodiversity and informing conservation strategies. Collaborations span institutions globally, reflecting her interdisciplinary approach to solving ecological problems.

Steve ShirtliffeView profile

Professor

• Field crop agronomy
• Crop Phenotyping
• Crop Imaging
• +3 more

Steve Shirtliffe is a Professor in the Department of Plant Sciences at the University of Saskatchewan’s College of Agriculture and Bioresources. His research focuses on field crop agronomy, crop phenotyping, precision agriculture, and weed science. He holds a Ph.D., M.Sc., and B.Sc. from the University of Manitoba. Dr. Shirtliffe’s work integrates remote sensing, machine learning, and field experimentation to improve crop yields and agricultural sustainability. He has pioneered applications of UAV-based imaging for crop analysis, including canola yield prediction and weed management. His research emphasizes precision agriculture, crop lodging prediction, and mechanical weed control systems. He has received prominent awards including the ASA Professor of the Year (2008) and University of Saskatchewan’s Provost’s Outstanding Teaching Award (2011-12). Courses taught include PLSC 375 (Current Topics in Agronomy), PLSC 401 (Sustainable Crop Production), and PLSC 898 (Plant Population Biology). Recent research highlights include: Development of AI-driven field boundary delineation systems Integration of SAR and optical data for crop phenology mapping Optimized deep learning models for edge device yield prediction Long-term weed dynamics studies in organic/conventional systems His work addresses critical challenges in Prairie agriculture through innovative technology applications and systems-level crop management strategies.

Juan EncisoView profile

Professor

• Irrigation System Design
• Water Management
• Deficit Irrigation
• +3 more

Juan Enciso is Professor of Biological and Agricultural Engineering at Texas A&M University, specializing in irrigation system design, water management, and precision agriculture technologies. His research develops UAS-based remote sensing methods for crop monitoring, yield prediction, and resource optimization. Current projects include AI-driven detection of crop contaminants, wastewater utilization for bioenergy crops, and citrus yield estimation using multi-temporal datasets. Enciso's work bridges engineering solutions with sustainable agricultural practices, particularly in water-scarce regions. He has extensively studied deficit irrigation strategies, salinity management, and UV treatment of agricultural water. Recent publications demonstrate advanced applications of deep learning for volunteer plant detection and plastic contaminant mapping in agricultural fields.

• Traffic Conflict Analysis
• Connected and Automated Vehicles
• Human Factors and Driving Behaviour
• +5 more

Professor Md. Mazharul Haque is a distinguished academic and researcher in transportation engineering at Queensland University of Technology (QUT), Australia. He serves as Head of the School of Civil & Environmental Engineering and holds a Professorial rank. His expertise spans econometrics, artificial intelligence, and traffic safety. He has secured over $3 million in research funding, published over 110 peer-reviewed articles, and achieved an h-index of 27 (Scopus) and 33 (Google Scholar). His work is recognized through prestigious roles like Associate Editor of Accident Analysis & Prevention and ASCE Journal of Transportation Engineering . Research focuses include Traffic Conflict Analysis, Connected and Automated Vehicles (CAVs), Human Factors in Driving, and Black Spot Identification. He co-founded the startup Advanced Mobility Analytics Group (AMAG), developing AI-driven road safety tools. Awards include the TRB Outstanding Paper Award (2020, 2016) and the John Kirby Award (2014). He has advised multiple PhD students on topics like CAV integration, safety modeling, and traffic flow optimization. His grants address real-time risk assessment, safety evaluation of traffic signals, and CAV behavioral impacts. Education includes a PhD from the National University of Singapore (2005–2009) and degrees from Bangladesh University of Engineering and Technology (BUET). Professional memberships include TRB Committees, ASCE, and editorial roles in leading journals. Key contributions include pioneering AI-based video analytics for crash risk forecasting and advancing econometric models for transportation systems.

Les MayhewView profile

Professor

• Actuarial Science
• Demography
• Statistics
• +13 more

Les Mayhew is a part-time Professor of Statistics at Bayes Business School (formerly Cass), City, University of London, within the Faculty of Actuarial Science and Insurance. He is an Honorary Fellow of the Institute of Actuaries and a member of the Royal Economic Society. His career spans academia, government, and consultancy, with significant contributions to demography, pensions, health, and long-term care policy. His research interests include demographic methods, population ageing, the economics of ageing, and the exploitation of administrative data for population estimation and policy evaluation. He has pioneered the use of large administrative datasets and Geographical Information Systems in public sector research, particularly in health and local government applications. His work often bridges actuarial science, statistics, and public policy. The recent articles reflect a strong focus on health inequalities, longevity risk, pension sustainability, and innovative financing models for long-term care. Key themes include the impact of smoking on healthy life expectancy, gender convergence in survival, deprivation and lifespan inequality, and asset-based solutions for care funding. His research combines rigorous statistical analysis with practical policy implications. Twice winner of the Bayes research prize Winner of the University Project of the Year prize Joint winner of the University Research Impact Award Honorary Fellow of the Institute of Actuaries Les Mayhew advises central government, healthcare providers, and local authorities on policy evaluation, resource allocation, and service commissioning. He is the Managing Director of Mayhew Harper Associates Ltd., a research consultancy specializing in data linkage. He has supervised and collaborated with numerous researchers and students, though specific names are not listed. He has held visiting appointments at IIASA and the University of Newcastle, and previously served in senior roles at the Office for National Statistics and the Department of Social Security. He has led projects on population estimation using administrative data, transport policy (including the London congestion charge), and the economic impacts of population ageing. His future work appears focused on levelling up health outcomes, sustainable care systems, and the economic implications of longevity.

Matteo BoniniView profile

Assistant Professor

• Algebraic Geometry
• Coding Theory
• Finite Fields
• +8 more

Matteo Bonini is an Assistant Professor in the Department of Mathematical Sciences at Aalborg University, under the Faculty of Engineering and Science. He is also associated with the AAU BLUE – Marine & Maritime Research initiative, indicating interdisciplinary engagement in real-world applications of mathematics. His research lies at the intersection of algebraic geometry, finite fields, and coding theory. PhD in Mathematics – Specific institution and year not mentioned in text Possibly postdoctoral experience inferred from publication history and project role His research interests center on algebraic geometry , coding theory , and combinatorics , particularly focusing on AG codes, covering codes, rational points on algebraic varieties, irreducibility of polynomials, and weight distributions in linear codes. His work often applies algebraic methods to solve problems in information theory and error correction. The fingerprint analysis from his profile highlights recurring themes such as Polynomial Mathematics, Weight Distribution, Rational Points, Linear Codes, and Algebraic Geometry. The recent publications show a consistent trend in exploring structural properties of codes—especially covering codes in sum-rank metrics, minimal codewords, and connections between algebraic curves and code design. His studies frequently involve deep algebraic techniques over finite fields, with applications in cryptography and data transmission. He collaborates internationally, as evidenced by co-authorships across European institutions. Currently, there are no scientific awards mentioned in the provided text. Matteo Bonini serves as a Principal Investigator in the ongoing project Blue Math: Blue Mathematics at AAU , which aims to apply advanced mathematical methods to marine and maritime challenges. While no formal students are listed, his role as a PI and active researcher suggests mentorship responsibilities. No specific grants are detailed beyond this project. He is affiliated with the Mathematics group within the Department of Mathematical Sciences at Aalborg University, contributing to both theoretical and applied research, particularly through the AAU BLUE initiative that connects mathematical modeling with engineering and real-life systems.

• Coding Theory
• Algebraic Geometry Codes
• Finite Fields
• +7 more

Hans Olav Geil is a Professor and Pro-dean at the Faculty of Engineering and Science , Aalborg University , Denmark, affiliated with the Department of Mathematical Sciences . He is actively engaged in research, academic leadership, and educational innovation, particularly in coding theory and engineering education. University: Aalborg University School: Faculty of Engineering and Science Department: Department of Mathematical Sciences Email: prodekan-eng-udd@aau.dk ORCID: 0000-0002-9666-3399 Website: http://people.math.aau.dk/~olav Geil's research centers on algebraic coding theory , with significant contributions to affine variety codes , quantum codes , network coding , and finite fields . His work bridges theoretical mathematics and practical applications in data transmission and cryptography. He has developed methods for code construction, decoding, and performance bounds, particularly using Gröbner bases and algebraic geometry. His recent publications (2020–2023) show a dual focus: (1) advanced coding techniques like Steane-enlargement of quantum codes and nested code pairs, and (2) educational research on project-based learning (PBL) , digital transformation, and microcredentials at Aalborg University. This reflects his leadership in both technical research and pedagogical innovation. The body of his work, including over 79 publications, demonstrates consistent contributions to IEEE Transactions on Information Theory , Designs, Codes and Cryptography , and Finite Fields and Their Applications . His research often involves collaborations with experts in coding and cryptography. Geil has supervised several PhD students and is involved in multiple research projects. Though no specific awards are listed in the text, his editorial roles and sustained publication record indicate recognition in the academic community. He leads initiatives like the Vision-in-Practice Project , aiming to transform engineering education through structured frameworks and continuous improvement. His work supports interdisciplinary, problem-based learning models central to Aalborg University’s educational philosophy.

• Election Auditing
• Statistical Methods
• Voting Systems
• +3 more

Damjan Vukcevic is an Associate Professor in the Econometrics & Business Statistics department at Monash University. His research focuses on statistical methods for auditing electoral systems, particularly in improving computational efficiency and integrity of voting processes such as Instant-Runoff Voting (IRV) and Single Transferable Vote (STV). He leads the ARC-funded project In for the count: Maximising trust and reliability in Australian elections (2022–2026), collaborating with experts in computer science and political science to enhance election validation techniques. His work integrates advanced statistical modeling, including Dirichlet-Tree models and Bayesian methods, to address challenges in election auditing. Recent projects emphasize risk-limiting audits (RLAs) for multi-seat and ranked-choice systems, ensuring transparency and accuracy in electoral outcomes. Co-authored papers span topics like adaptive auditing algorithms, KIR gene imputation, and genomic studies, though his primary focus remains electoral integrity. Collaborations include international conferences such as Financial Cryptography & Data Security and E-Vote-ID, where his team presents cutting-edge solutions for election security. Despite no listed awards, his prolific output (49+ publications since 2006) underscores his influence in computational statistics and electoral science.

Kjetil RøyslandView profile

Professor

• Causal Inference
• Survival Analysis
• Health Registries
• +1 more

Kjetil Røysland is a Professor at the University of Oslo, specializing in causal inference methodologies applied to survival analysis and clinical health registry data. His research focuses on developing statistical techniques for evaluating treatment effects, particularly in continuous-time frameworks involving survival data and event history analysis. He employs advanced mathematical tools such as counting processes, stochastic differential equations, and graph theory to address causal questions in healthcare. His work emphasizes methodological contributions to biostatistics, including causal mediation analysis, time-dependent confounding adjustment, and continuous-time marginal structural models. Røysland collaborates extensively with clinical researchers and contributes to health registries' utilization for improving evidence-based medical decisions. Røysland is affiliated with the Causal Inference and Event History Analysis research group. His publications span high-impact journals like the Journal of the Royal Statistical Society and Biometrics, reflecting his expertise in bridging theoretical statistics with practical healthcare applications.

• Robotics
• Reinforcement Learning
• Multi-Agent Systems
• +4 more

Joni Pajarinen is an Associate Professor in the Department of Electrical Engineering and Automation at Aalto University. His research focuses on reinforcement learning, robotics, and autonomous systems with applications in multi-agent coordination, control systems, and object-centric learning. He has contributed to advancements in policy optimization, imitation learning, and underwater vehicle control. Key research areas include: Reinforcement learning algorithms for robotics Multi-agent systems and cooperative control Autonomous underwater vehicle navigation Machine learning for industrial applications His recent work emphasizes scalable reinforcement learning frameworks, such as AgentMixer for correlated policy factorization and entropy-regularized task representations. He explores vision-language models (e.g., RGB-Th-Bench) and large-scale motion datasets (RP1M) for humanoid robotics. Publications span top venues like AAAI, ICLR, and IEEE Robotics and Automation Letters. His research bridges theory and real-world applications in robotics, marine engineering, and manufacturing automation.

• Image Processing
• Machine Learning
• Computer Vision
• +4 more

Saturnino Maldonado Bascón is a full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department. His research focuses on advanced signal processing techniques, machine learning applications, and computer vision systems. He earned his doctorate from Universidad de Alcalá in 1999 with a thesis on multiresolution analysis for image compression. Key research interests include traffic sign recognition, video surveillance algorithms, 3D object recognition, and noise reduction in digital images. His work frequently employs support vector machines (SVM), spatial pyramids, and clustering methods to address challenges in robotics, autonomous systems, and sensor data analysis. Publications highlight contributions to vehicle tracking, odometry correction in differential robots, and efficient video annotation techniques. His research group (GRAM) develops multisensorial analysis solutions for intelligent infrastructure systems like the WHISNU platform. Maldonado has explored diverse applications from biomedical signal processing to traffic management systems through grants and collaborative projects. No scientific awards are explicitly mentioned, but his extensive publication record reflects sustained academic impact. He leads a research team focused on real-time systems, sensor fusion, and computer vision applications in both academic and industrial contexts.

• Financial Econometrics
• Time Series Analysis
• Large Dimensional Financial Data Analysis
• +4 more

Professor Ruijun Bu is a Professor of Econometrics at The University of Liverpool Management School (ULMS). He holds a Bachelor’s degree in Engineering from Tongji University, China, and a Master’s in Finance and a Ph.D. in Economics and Finance from The University of Liverpool. His research focuses on financial econometrics, time series analysis, large-dimensional data, nonparametric statistics, quantitative finance, and energy economics. He has held significant roles, including Director of Research for the Economics Group at ULMS and Founder/Director of the Econometrics and Big Data research cluster (2012–2021). He has secured grants from the ESRC, British Academy, and others. His work has been published in top journals like the Journal of Econometrics and Energy Economics . Bu has collaborated with institutions such as Princeton University (as a Visiting Research Fellow) and international universities on topics like regime-switching models and energy economics. He is an Associate Editor of Economic Modelling (2020–present) and has contributed to academic committees and teaching modules in econometrics and financial economics. Education: Bachelor’s in Engineering, Tongji University Master’s in Finance, University of Liverpool Ph.D. in Economics and Finance, University of Liverpool Research Interests: Financial Econometrics Time Series Analysis Large-Dimensional Data Analysis Non- and Semi-Parametric Statistics Quantitative Finance Empirical Finance Energy Economics Grants and Awards: Dissecting Systemic Risks in Large Economic Sectors (British Academy, 2022–2024) Modelling Interest Rate Dynamics (ESRC, 2012–2014) Teaching and Advising: Supervised theses on econometric models and transformed diffusion applications. Teaches modules like ECON311 (Time Series Econometrics) and ECON308 (Quantitative Financial Economics). Labs and Collaborations: Led the Liverpool Advanced Methods for Big Data Analytics (LAMBDA) Research Centre. Collaborations with Professors at institutions like Keele University, University of Lille, and Sungkyunkwan University.

• Panel data analysis
• Time series analysis
• Financial Econometrics
• +3 more

Dr. Yao Rao is a Senior Lecturer in Econometrics at the University of Liverpool Management School, holding a Master's in Finance and PhD in Economics. He is a Fellow of the Higher Education Academy and Royal Statistical Society. His research focuses on panel data analysis, time series modeling, financial econometrics, and statistical applications in public health. He has held visiting roles at Imperial College London and contributed to prestigious journals like the Journal of the Royal Statistical Society Series C and Journal of Time Series Analysis. Dr. Rao serves on multiple academic committees, including the Royal Statistical Society Local Group and University of Birmingham External Examiner. His professional memberships include the Econometric Society and Institute of Risk and Uncertainty. He coordinates the Methods of Economic Investigation 2 module and leads research initiatives within the Liverpool Advanced Methods for Big Data Analytics (LAMBDA) Research Centre. His work spans econometric theory, financial modeling, and healthcare applications, with recent publications addressing serial correlation in factor models, inflation forecasting using threshold MIDAS, and pandemic response strategies. He has developed novel methodologies for structural break detection and cross-covariance estimation, contributing to both academic and applied econometric practices.